University of Nevada, Reno

1. University of Nevada, Reno Seminar Topic: Characterization of Mineral Dust using XRD and Infrared Spectroscopy Case study: Ilam, Iran Presenter: Mohammad Reza Sadrian Professor Patrick Arnott Professor Mae Gustin ATMS 790 - Graduate Seminar in Atmospheric Sciences Spring 2019

2. Outline • Introduction • Past studies • Proposed Work: • Sample Location • Sample collection • Methods (Spectroscopy and XRD) • Initial Results ( Using SWIR, LWIR, and XRD)

3. Sources of Air Pollution

4. Introduction Electron-microscope images of sample mineral Cloud Condensation Nuclei • Mineral dust • Health Hazard and Environment Effects: • Direct and Indirect effects on radiation: • Different type of dust: (PM10 and PM2.5) • Common sources: Biomass burning, automobile, and etc… • PM2.5: Incomplete combustion of petroleum products • PM10: Soil dust, unpaved road dust

5. Dust moves through several processes:  Saltation Creep Suspension

6. Let’s see how dust storms happen in desert area:Link:https://www.youtube.com/watch?v=T_oqoxGHIc0Link:https://www.aparat.com/v/Zlq3m/%D8%B7%D9%88%D9%81%D8%A7%D9%86_%D8%B4%D9%86_%D8%AF%D8%B1_%D8%B4%D9%87%D8%B1_%DB%8C%D8%B2%D8%AF

8. Location of 13 (Marble Dust Collector) MDCO deposition sites (S1-S13) within Ilamcity Collected in different 3 seasons Total of 37 samples

9. Locality of 13 deposition samplers in Ilam city.

10. Sample Collection Aluminum cover Marble Small brush Sample bottle MDCO (Marble Dust Collector) airborne dust collector Placing the marbles in sampler (Goossensand Offer, 2000) and (Goossens et al., 2001).

11. Three monthly deposition rates in three periods of sampling ? 4832 metric-tons (mt) over the entire city of Ilam (fall) 5328 mt (winter) 7233 mt(spring) 1 mt = semi truck? 1 metric ton = 1000 kg*

12. Significance of the Project: Dust composition is poorly known and not linked to source regions. Global soil mineralogy is not well defined. How mineralogy changes with dust particle size is also unknown. In particular SWIR measurements of mineral dust have not been previously made. Measurements will define dust composition using tools that can be linked to global remote sensing to understand transport. Spectral measurements can generate optical constants for use in atmospheric models.

13. Preliminary Work • Analytical Spectral Device (ASD): • In the visible, near infrared, and short-wave infrared (VNIR/SWIR) (0.4 to ~2.5 m), absorption features arise due the electron orbital configuration of transition metals. • Fourier-Transform Infrared (FTIR ) Spectrometer: • The long-wave infrared (LWIR) (5 to 25 m, or 2000 to 400 cm-1), is sensitive to the fundamental molecular vibrations of ligand groups similar to the VNIR/SWIR. Analytical Spectral Devices • The proposed research uses XRD to identify the presence of mineral in a dust sample. Nicolet TM 6700 • Techniques Methods • Spectra (ASD and FTIR) • XRD Bruker AXS D2 phaser

14. Sampling Strategy with ASD Sample holder Schematic illustration of the technical arrangement of a hyperspectral field- or laboratory measurement (Danner et al, 2015) Contact probe

15. How FTIR works The FTIR uses interferometry to record information about a material placed in the IR beam. The Fourier Transform results in spectra that analysts can use to identify or quantify the material. • An FTIR spectrum arises from interferograms being ‘decoded’ into recognizable spectra • Patterns in spectra help identify the sample, since molecules exhibit specific IR fingerprints (Kokaly, R.F., et al. 2017)

16. How XRD works!

17. An Infra-Red spectroscopic view of atmospheric particulates over El Paso, Texas Past Studies Nine sets of atmospheric dust samples were collected at 5 km intervals along an east-west line from commercial, high population density residential, industrial, and low population density residential environments within the central urban area of El Paso, Texas Blanco and McIntyre, 1972

18. Frequency of identification as a function of particle size for each member of the five chemical families in the nine atmospheric dust samples containing a given constituent is indicated by the number in the appropriate column.

19. Transmittance Method by stage 6 the silicate (1028 cm^-l) absorption band is no longer the strongest band. All sets of samples revealed the presence of ammonium sulfate in the large particle fraction and never in the giant particle fraction of the sample. Infra-red absorption spectra of atmospheric dust collected, sample 5 with stage l-6.

20. Past Studies (Bharti, et al., 2017) Characterization and morphological analysis of individual aerosol of pm10 in urban area of lucknow, India. Particulate matters were studied for morphological analysis, elemental composition and functional group variability with the help of Scanning Electron Microscope-Energy Dispersive Spectroscopy (SEM-EDS) followed by Fourier Transform Infrared spectroscopy (FTIR). Different sampling sites at Lucknow

21. Past Studies • Sulphate and bisulphatewas found integrated between 1000–1200 cm−1 and 605–595.8 cm−1respectively. • Strong absorption at 1090–730 cm−1 represents the dominance of quartz and kaolinite in the particulates. • Absorption between 461 and 467 cm−1 indicates the presence of silicate ions in particulate matter, whereas, absorption between 776 and 796 cm−1 is due to presence of silica. FTIR spectra of particulate matter collected from various locations of Lucknow (NG =Nishatganj, MG =Mohanlalganj) (Bharti et al. 2017)

22. Past Studies Physical properties of airborne dust over Arabian Red Sea coastal plain Position of (a) the King Abdullah University of Science and Technology (KAUST) campus on the Arabian Peninsula (red marker), north of the coastal city of Jeddah, and (b) the Frisbee deposition sites (DT1-DT4) on the KAUST campus (Engelbrecht, et al., 2016)

23. Sampling Method Inverted Frisbee-type deposition sampler (a) on tripod and white plastic drainage bottle. (b) View showing the foam insert in the collection dish to help retain the deposited dust particles, as well as the spikes with nylon thread to prevent birds from readily perching on the dish.

24. Gravimetric analysis Monthly deposition rates (gm-2) from Frisbee samplers (DT1-DT4) at the KAUST campus. Also shown are the monthlyaverages for the four samplers.

25. ? Mineral Analysis by XRD Semi-quantitative XRD mineral analyses of monthly Frisbee samples collected at the three sites DT1–DT3, for the period December 2014 to December 2015.

26. Analysis to Date • Measured all 37 using XRD, SWIR and LWIR reflectance • Analysis Approach • Group spectra by similar features (by ENVI software) • Identify common features using libraries • XRD uses instrument evaluation software to ID minerals

27. SWIR USGS Spectral Library CO2 CO2 CO2 CO2

28. Preliminary Results for SWIR No CO2 is detected

29. Preliminary Results for XRD 26.5̊ 29.5̊ RRUFF Database

30. Initial spectral results: S3.Sep23rd-Dec21st Calcite Chlorite Illite Kaolinite S8.Sep23rd-Dec21st Calcite Chlorite Illite Kaolinite

31. Initial spectral & XRD results: From Library American Mineralogical Crystal Structure Database S9.March20th-June20th

32. What about quartz fingerprint in SWIR? 26.5̊ The peak on 26.5̊ is attributed to quartz

33. Samples measured in my preliminary analysis by XRD, SWIR, and LWIR

34. LWIR Reflectance Interpretation still to be done.

35. Future Work Transmittance Technique Verification of the spectroscopy against previously well characterized samples will allow development of new, rapid methodologies for future dustsample mineral identification. Contrasts reflectance (dashed lines) of fine-grained material vstransmission measurements of the same sample. Multiple scattering enhances weak features and may obscure fundamental vibrational absorptions which are clearly observed in transmission (solid lines).

36. References • Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., Driscoll, R.L., and Klein, A.J., 2017, USGS Spectral Library Version 7: U.S. Geological Survey Data Series 1035, 61 p., https://doi.org/10.3133/ds1035. • Pidwirny, M. (2006). "Atmospheric Effects on Incoming Solar Radiation". Fundamentals of Physical Geography, 2nd Edition. Date Viewed. http://www.physicalgeography.net/fundamentals/7f.html • Nousiainen, T. (2009). Optical modeling of mineral dust particles: A review. Journal of Quantitative Spectroscopy and Radiative Transfer, 110(14), 1261-1279. doi:10.1016/j.jqsrt.2009.03.002 • Goossens, D., Offer, Z.Y., Wind tunnel and field calibration of six aeolian dust samplers, Atmospheric Environment 34(2000), pp. 1043-1057. • Blanco, A. J., & McIntyre, R. G. (1972). An infra-red spectroscopic view of atmospheric particulates over el paso, texas. Atmospheric Environment (1967), 6(8), 557-562. doi:10.1016/0004-6981(72)90073-X • Bharti, S. K., Kumar, D., Kumar, N., Anand, S., Poonam, & Barman, S. C. (2017). Characterization and morphological analysis of individual aerosol of PM10 in urban area of lucknow, india. Micron, 103, 90-98. doi:10.1016/j.micron.2017.09.004 • Engelbrecht, J., Stenchikov, G., Prakash, P., Lersch, T., Anisimov, A., & Shevchenko, I. (2017). Physical and chemical properties of deposited airborne particulates over the arabian red sea coastal plain. Atmospheric Chemistry and Physics, 17(18), 11467-11490. doi:10.5194/acp-17-11467-2017 Danner, M.; Locherer, M.; Hank, T.; Richter, K. (2015): Spectral Sampling with the ASD FieldSpec 4 – Theory, Measurement, Problems, Interpretation. EnMAP Field Guides Technical Report, GFZ Data Services. http://doi.org/10.2312/enmap.2015.008

37. Thank you!Questions?